Streamline your sales forecast automation for security

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In 2020, Nvidia launched  their new generation GPUs — GeForce RTX 3080.   Four days after the release, the company  said that they weren’t ready for such   high demand. Nor were their partners. Many  reported record traffic to their websites.   We all know what happened next  --the global chip supply shortage.   Hello, I’m Kirill Stati, the managing  director for data and analytics at   AltexSoft. And we continue our series  of videos on the practical use cases   of artificial intelligence and  machine learning in business.   As you may have guessed, today we’re going  to talk about demand and sales forecasting.   Let’s start! Demand and sales forecasting   The world is changing with overwhelming  speed — faster than ever before. This   makes the future even more unpredictable. We live in a time when a series of Reddit   posts and comments can shake  up the global stock market.   When it takes a single tweet from  Elon Musk to breathe life into   bitcoin after it hit a three-month low. When a teenage girl can start the “flight   shame” movement and cause a downturn in  domestic air travel in Germany and Sweden.   Situations like these are getting more  common. And then there’s the pandemic   that paralyzed the world's supply chains. Of course, it's impossible to predict another   Elon Musk tweet, let alone its consequences.  But there are a lot of things businesses can   prepare for in advance. Especially when the  right approach to demand and sales forecasting   is up their sleeve. But what are demand   and sales forecasting in the first place? Demand forecasting is a practice used to  predict the market demand for a specific   product or service over a defined period. Sales forecasting deals with projecting   success in sales and whether you  will be able to meet the demand.   In plain English, a demand forecast is how many  people will want your product and a sales forecast   is how much of it you can sell. Smart companies employ both   practices to sell their goods and services. But even for a small home improvement store,   such predictions are difficult to make.  There are too many things to consider.   These may be local real estate, seasonality,  and geographic position, to name a few.   And if you miss the mark  with any of these factors,   you miss the mark with overall demand  forecasting. This often results in   situations when a company produces  not enough or too many products.   On a global scale, overstocks and out-of-stocks  cost retail companies billions of dollars.   But don't just take my word for it. Here's an infamous Nike case. In 2001,   the company decided to demand-planning  software without proper testing. As a result,   there was an overstock of low-selling shoes and  a deficit of the popular Air Jordans. The shoe   manufacturer lost a $100 million in sales. The takeaway is obvious. Accurate demand and   sales forecasting is kind of a big deal. But how exactly can we make these sorts of   predictions? And more importantly, how  to make them as accurate as possible?   Actually, there are several approaches.  It may not seem so, demand and sales forecasting  are far from new concepts. Many companies have   been using traditional statistical methods  in forecasting processes for years.   Automated and coupled with the  opinions of professional analysts,   such methods work fine. But only under  one condition -- stability.   Statistical methods deal with time series. This  means the future demand is statistically estimated   using the past demand data. That’s why traditional  forecasting is often referred to as historical.   Say, we can assume that eggs and chocolate will  experience their annual demand spike in spring.   The past data tells us that it  happens every year before Easter.   But as we already said, there are a lot of  uncontrollable external factors affecting   demand. And past values can't always  represent such signals with accuracy.   For instance, UBER can't forecast demand  for their ride services relying only on   time series data. The ride-hailing company  takes into account all sorts of information   from times of the day and times of the week  to weather conditions and city events.   Okay, statistical methods don't work  when it comes to external data and   frequent changes. But then what does? Today, companies are actively embracing   AI and ML-driven systems, and for a reason.  They make it possible to bring forecasting   automation to a whole new level. But there’s  more to that. ing to the McKinsey report,   implementing AI in supply  chain management can reduce   prediction error by 20 to 50 percent. The use of data, statistics, and machine learning  give birth to predictive analytics. It allows you   to consider all demand signals that come from  both internal and external sources. When we say   “external” we mean any data point from competitor  promotion campaigns to exchange rates.   Traditional methods alone fail to  capture such information. Just as   they fail to deliver accuracy since they can  use only a few past demand factors. Advanced   predictive analytics uses a sum of different  attributes. Also, it helps find out how much   each attribute influences demand. So, what about a fast-changing environment?  Machine learning has the upper hand here too.   With the method known as demand sensing, companies  can build a solution to manage real-time changes   and respond to them. Demand sensing tools capture  real-time data from POS and other systems.   They compare collected information to historical  patterns to detect any demand spikes and drops.   Then systems decide whether a particular deviation  is significant and offer updates to forecasts.   Let's take Luxottica, for example. Each  year the eyewear industry leader adds   2000 new products to its collection. Of  course, the company analyzes the behaviors of   past launches to forecast demand. But that’s not  all. Its machine learning system learned behavior   patterns during the first launch period too.  That’s how Luxottica reduced the demand forecast   error on new launches by around 30 percent. Nike also applies AI in demand forecasting.   The company built a warehouse in the Los  Angeles area that uses predictive analytics   and real-time inventory tracking. In this  way, they can project changes in customer   demand and ensure the needed inventory is up  and ready for one- and two-day shipping.   Quite impressive, isn’t it? And all  thanks to AI and ML technologies.   Bet you’re already wondering how to  start forecasting demand this way.   The good news? There are  three different scenarios.   The first one is to pick the most fitting  off-the-shelf solution. There are quite a   few of them on the market. But keep one thing  in mind. Ready-made machine learning tools for   demand and sales forecasting are often tailored to  the needs of a specific industry. Such solutions   provide quite limited functionality. But it may  be enough to help you build more or less accurate   business predictions. At least at the beginning  of your journey to implementing AI automation.   So, here are a few big players to consider. * Logility Solutions,   * Blue Yonder, and * PredictHQ.   The next scenario is to go with semi-ready  solutions like Amazon Forecast or Azure   Machine Learning. These platforms will provide  your IT team with a set of ML-powered tools   to build efficient demand and sales  predictions. Yet, solutions in this   category mostly work with historical data and  can’t consider all possible external factors.   Custom development with an R&D data science  team is the third and perhaps best way to   use the power of machine learning. This scenario  gives you a solution tailored to the uniqueness   of your company and its specific needs. You will be able to add as many external factors   as you require that may affect the demand. Besides  that, you can include unique customer profiles.   Planning demand with consumer likes and dislikes  in mind will help increase sales efficiency.   Let’s take McDonald’s for example. The company invests in research and   development and technology to increase customer  satisfaction. The fast-food giant has acquired   Dynamic Yield — a tech company that builds  AI-powered software. Together they created   a unique technology to customize menu displays  based on variables such as weather conditions,   times of the day, and previous customer choices. Of course, custom development requires lots of   time, investment, and effort. But this approach  guarantees the most accurate prediction results.   After all, demand forecasting accuracy is the key  to an optimized supply chain. It enables a close   match between the supply and the demand. So, let’s wrap things up.   Driven by artificial intelligence and machine  learning, demand and sales forecasting models help   automate time-consuming tasks, reduce forecast  errors, optimize inventory, and increase sales.   Companies with quality ML prediction mechanisms  have hammered their competitors big time. Think   of eCommerce giants like Amazon and Alibaba that  were the pioneers in AI technology implementation.   And with the right R&D team by your side, you can  become the “Amazon” of your industry. Or at least   approach that rung on the ladder. Okay, thank you for watching! Hit the “Like”  button if you enjoyed this video. Also,   leave your comments and questions in the  comment section below. And see you next time.